def load(cls, path: Text) -> "EmbeddingPolicy":
"""Loads a policy from the storage.
**Needs to load its featurizer**
"""
if not os.path.exists(path):
raise Exception("Failed to load dialogue model. Path '{}' "
"doesn't exist".format(os.path.abspath(path)))
featurizer = TrackerFeaturizer.load(path)
file_name = "tensorflow_embedding.ckpt"
checkpoint = os.path.join(path, file_name)
if not os.path.exists(checkpoint + ".meta"):
return cls(featurizer=featurizer)
meta_file = os.path.join(path, "embedding_policy.json")
meta = json.loads(rasa.utils.io.read_file(meta_file))
with open(os.path.join(path, file_name + ".tf_config.pkl"), "rb") as f:
_tf_config = pickle.load(f)
graph = tf.Graph()
with graph.as_default():
session = tf.Session(config=_tf_config)
saver = tf.train.import_meta_graph(checkpoint + ".meta")
saver.restore(session, checkpoint)
a_in = train_utils.load_tensor("user_placeholder")
b_in = train_utils.load_tensor("bot_placeholder")
sim_all = train_utils.load_tensor("similarity_all")
pred_confidence = train_utils.load_tensor("pred_confidence")
sim = train_utils.load_tensor("similarity")
dial_embed = train_utils.load_tensor("dial_embed")
bot_embed = train_utils.load_tensor("bot_embed")
all_bot_embed = train_utils.load_tensor("all_bot_embed")
attention_weights = train_utils.load_tensor("attention_weights")
return cls(
featurizer=featurizer,
priority=meta.pop("priority"),
graph=graph,
session=session,
user_placeholder=a_in,
bot_placeholder=b_in,
similarity_all=sim_all,
pred_confidence=pred_confidence,
similarity=sim,
dial_embed=dial_embed,
bot_embed=bot_embed,
all_bot_embed=all_bot_embed,
attention_weights=attention_weights,
**meta,
)
def load(
cls,
meta: Dict[Text, Any],
model_dir: Text = None,
model_metadata: "Metadata" = None,
cached_component: Optional["EmbeddingIntentClassifier"] = None,
**kwargs: Any,
) -> "EmbeddingIntentClassifier":
if model_dir and meta.get("file"):
file_name = meta.get("file")
checkpoint = os.path.join(model_dir, file_name + ".ckpt")
with open(os.path.join(model_dir, file_name + ".tf_config.pkl"), "rb") as f:
_tf_config = pickle.load(f)
graph = tf.Graph()
with graph.as_default():
session = tf.compat.v1.Session(config=_tf_config)
saver = tf.compat.v1.train.import_meta_graph(checkpoint + ".meta")
saver.restore(session, checkpoint)
a_in = train_utils.load_tensor("message_placeholder")
b_in = train_utils.load_tensor("label_placeholder")
sim_all = train_utils.load_tensor("similarity_all")
pred_confidence = train_utils.load_tensor("pred_confidence")
sim = train_utils.load_tensor("similarity")
message_embed = train_utils.load_tensor("message_embed")
label_embed = train_utils.load_tensor("label_embed")
all_labels_embed = train_utils.load_tensor("all_labels_embed")
with open(
os.path.join(model_dir, file_name + ".inv_label_dict.pkl"), "rb"
) as f:
inv_label_dict = pickle.load(f)
return cls(
component_config=meta,
inverted_label_dict=inv_label_dict,
session=session,
graph=graph,
message_placeholder=a_in,
label_placeholder=b_in,
similarity_all=sim_all,
pred_confidence=pred_confidence,
similarity=sim,
message_embed=message_embed,
label_embed=label_embed,
all_labels_embed=all_labels_embed,
)
else:
warnings.warn(
f"Failed to load nlu model. "
f"Maybe path '{os.path.abspath(model_dir)}' doesn't exist."
)
return cls(component_config=meta)
